基于大语言模型的规则协同分析：研究现状与启示
Rule Synergy Analysis using LLMs: State of the Art and Implications
AI总结：
本文通过《Slay the Spire》卡牌协同数据集评估LLMs对复杂规则交互的推理能力，发现其在识别正负协同方面存在明显不足，并归纳错误类型，为改进规则交互预测提供研究方向。
摘要：
大语言模型（LLMs）在逻辑推理、数学等多个领域已展现出强大性能。本文考察LLMs在动态环境（如卡牌游戏）中理解和推理复杂规则交互的能力。我们引入了一个来自游戏《Slay the Spire》的卡牌协同数据集，其中卡牌对根据其正向、负向或中性交互进行分类。我们的评估表明，尽管LLMs擅长识别非协同卡牌对，但它们在检测正向协同、尤其是负向协同时表现不佳。我们归纳了常见错误类型，包括时机处理、游戏状态定义以及遵循游戏规则方面的问题。我们的发现为未来研究指明了方向，以提升模型在预测规则效果及其交互方面的性能。
英文摘要：
Large language models (LLMs) have demonstrated strong performance across a variety of domains, including logical reasoning, mathematics, and more. In this paper, we investigate how well LLMs understand and reason about complex rule interactions in dynamic environments, such as card games. We introduce a dataset of card synergies from the game Slay the Spire, where pairs of cards are classified based on their positive, negative, or neutral interactions. Our evaluation shows that while LLMs excel at identifying non-synergistic pairs, they struggle with detecting positive and, particularly, negative synergies. We categorize common error types, including issues with timing, defining game states, and following game rules. Our findings suggest directions for future research to improve model performance in predicting the effect of rules and their interactions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
